Method and device for calculating trust values on purchases

ABSTRACT

Trust values of n nodes are calculated by relating nodes having corresponding relations with arrows. Pij (i, j=1 to n) is assigned as a weight for an arrow from node ui to node uj on the basis of the relation between the nodes. P′=CPcP+(1−Cc) E is calculated, where Cc=constant, E=a predetermined matrix, pP=a matrix having elements at row i and column j represented by Pij. The trust value of each node is calculated on the basis of P′.

RELATED APPLICATIONS

The present application is based on, and claims priority from, JapaneseApplication Number 2003-368802, filed Oct. 29, 2003, the disclosure ofwhich is hereby incorporated by reference herein in its entirety.

FIELD OF INVENTION

The present invention relates to a trust value calculating method anddevice for defining trust values of various elements concerned withconsumption activities of daily life and for defining trust values amongthe elements and for enabling consumers, etc. to make their best choiceson the basis of the trust values.

BACKGROUND OF INVENTION

Various elements, such as the motive of consumers, advertising media,shops, manufacturers, experts (critics), etc. are involved with oneanother in human daily consumption activities. In this document, thesefactors concerned with consumption activities are referred to as“subjects.” These subjects have various relations with one another. Itis assumed there is some kind of trust relation among these subjects.Consumers can obtain various information indicating the relations amongthese subjects. A commercial article A is made by a manufacturer B, andsold at a shop C. In a mode of the prior art, manufacturer B advertisesthe commercial article A by using the Internet, and shop C advertisesthe commercial article A by utilizing inserts in local newspapers.

Furthermore, a critic D analyzes commercial article A and places theanalysis in magazine E. The same consumption activities are carried outon other goods, and considerable information about the article,manufacturer, shop, expert and the magazine is available. Consumersfrequently cannot judge which manufacturer, shop, expert or the like canbe trusted. In order to solve the above problem, the prior art hasprovided an indicator or information about credibility of manufacturers,shops, experts, etc.

It is more advantageous to obtain information about the trust valuesrelevant to “subjects” in the consumption activities, and informationabout personal taste of the above information. It is known to useindividual personal taste information about the to obtain the mostcredible information about consumption activities.

Prior art disclosing the foregoing include:

(1) Laid-open JP Patent Publication (P1999-306185)

(2) Lawrence Page; Sergey Brin; Rajeev Motwani; Terry Winograd. ThePageRank Citation Ranking: Bringing Order to the Web. Technical Report.Stanford University, 1998.

(3) Noriko Arai; Kazuhiro Kitagawa. Personalization technique. NikkeiElectronics, 2003-02-03.

In JP P-1999-306185, PageRank is expanded so that texts and multimediadata and information of persons accessing these data are installed in alink structure as virtual web pages. In addition the web pages haveranking of information. When a user accesses various multimedia data onthe Internet to download the data or register the data as bookmarks, thelink structure of the web pages is expanded and the degree of importanceis calculated. In the PageRank method disclosed in the Page et aldocument , web pages on the Internet are defined as nodes, and the trustvalues thereof and the estimating method thereof are described. However,the PageRank method targets only the importance degree of the web pageson the Internet. Accordingly, there is no reflection of informationconcerning subjects which are not described on the Internet. Thus thePage Rank method does not always provide consumers with the bestinformation.

The Arai document introduces a personalization technique relying on userlikes and dislikes. According to this technique, goods are recommendedor introduced on the basis of the taste information of individuals. Araiet al includes a profile matching system, a rule base system, and acollaborative filtering system. According to these systems, goods whichseem to be best for individuals are recommended on the basis ofinformation such as individuals' taste information, purchase records,purchase patterns of general consumers, etc. However, in these methods,no consideration is paid to information about the trust values ofsubjects in consumption activities. Accordingly, an uncertain element asto whether it is truly good to purchase a recommended commercial articleor the like may remain regarding the recommended commercial article orthe like.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a new and improvedmethod of and apparatus for defining the relations among varioussubjects concerned with consumption activities, calculating trust valuesof these subjects and providing information which consumers can use inconsumption activities with high reliability.

Another object of the present invention is to provide a new and improvedmethod of and apparatus for providing information about subjects whichare coincident with tastes of individuals and credibly reflect tasteinformation of the individuals concerned.

According to one aspect of the present invention, trust values arecalculated without limiting the information to information on theInternet and by expanding the trust value information to variousrelations among subjects in consumption activities. In the PageRankmethod, the trust values of all the links are considered to beequivalent to one another. In contrast according to an aspect of thepresent invention there are definitions of the trust values of links,the type of the trust relation, and the trust value of each subject. Thetrust values are calculated by considering these matters.

The present invention differs from the disclosure of JP 1999-306185 byproviding trust values of information relevant to the web, and trustvalues of subjects having no direct relation with the informationrelation to the web and trust values of relations among these subjects.Further weighting is carried out on the basis of the type of relations.

According to a further aspect of this invention, credibility indicatorsfor manufacturers, shops, experts, etc. are determined by calculatingtrust values in the field of consumption activities. By referring tothese credibility indicators, a customer can select a shop ormanufacturer or a shop or a manufacturer can select media to placeadvertisements.

The trust values can be presented by responding to information on theInternet, and trust information from various data sources such asadvertisements, questionnaires, articles on magazines, etc. The trustvalues can be calculated by responding to the kind of trust relation orinformation about the subjects for which the trust degree is known inadvance.

The trust values can also be calculated by responding to the degree oftrust to various subjects which are owned by individuals. So-calledpersonalization based individualization can be performed. The trustvalue calculation can be performed by using an overall trust networkgraph created by collecting information and by using a partial trustnetwork graph concerning a partial subject group of subscribers of somespecific magazine or the like.

According to an additional aspect of the invention, goods which sellwell are estimated highly, and manufacturers, shops, experts, etc. whichare trusted by trusted manufacturers, shops, experts, and other subjectsare estimated highly, so that a more accurate estimate can be suppliedto customers. Furthermore, since the trust relation in the generalconsumption activities is modeled, the field of goods is not limited toa specific field, and is broadly applicable.

The above and still further objects, features and advantages of thepresent invention will become apparent upon consideration of thefollowing detailed description of the specific embodiments thereof,especially when taken in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a diagram including the concept of a trust network graph.

FIG. 2 is a general flowchart of operations performed by the apparatusof FIG. 7.

FIG. 3 is a flowchart of an algorithm that the apparatus of FIG. 7performs for calculating trust values.

FIG. 4 is a diagram of a first example of operations that the apparatusof FIG. 7 performs, wherein numeric values are filled in the trustnetwork graph.

FIG. 5 is a diagram showing a second example of operations that theapparatus of FIG. 7 performs, wherein numeric values are filled in thetrust network graph.

FIG. 6 is a diagram showing a third example of operations that theapparatus of FIG. 7 performs, wherein numeric values are filled in thetrust network graph.

FIG. 7 is a block diagram of a preferred embodiment of a device forperforming the present invention.

DETAILED DESCRIPTION OF THE DRAWING

FIG. 7 is a block diagram including a housing 200 containing a storagedevice 210, a main memory 220, an output device 230, a centralprocessing unit (CPU) 240, a console 250 and an input device 260. Thecentral processing unit (CPU) 240 reads a control program from the mainmemory 220, carries out information processing by responding to (1)commands from the console 250, user information from the input device260 and (3) trust data information stored in the storage device 210. CPU240 outputs information about the subjects with high reliability to theoutput device 230.

Web sites, consumers, advertisement media, shops, manufacturers, experts(critics), etc. (“subjects”) are concerned with the consumptionactivities of daily life. Estimates are made of the trust relations thatexist among these subjects.

FIG. 1 is a network diagram of consumption activities including“subjects” in the form of Internet sites, critics, goods, sellingoffices, manufacturers of goods, etc. All subjects in the consumptionactivities of FIG. 1 are selected as nodes. Furthermore, these subjectsare connected to one another by arrows, and the trust relations amongthe subjects and the trust values thereof are defined.

The subjects in the consumption activities and the trust relations amongthe subjects are extracted from various information sources, such asquestionnaires, web pages, magazines, electrical message boards, etc.The subjects are represented by using a network diagram in which thesubjects are represented as nodes and the trust relations arerepresented as arrows. FIG. 1 is referred to as a “trust network graphwithout trust values”. The trust degree of nodes and arrows arecalculated to determine trust values. The calculation is carried out byweighting the type of trust relations and, the trust degree of thenodes. Subsequently, the trust values thus calculated are filled in thetrust network graph. This is referred to as “trust network graph withtrust values”. On the basis of this information, information forrecommending/introducing goods is supplied to consumers and effectivemarketing information is supplied to sellers. The trust network graph isnot limited to the consumption activities, and can be applied tosubjects in various communities and the relations among them.

FIG. 1 is a trust network graph helpful in describing how the apparatusof FIG. 7 determines credibility indicators and trust values. “Subjects”such as web sites, consumers, advertisement media, shops, manufacturers,experts (critics), etc. which participate in the consumption activitiesare represented as nodes, and the trust relations among the subjects arerepresented as arrows. The pointing direction of an arrow is from atrusting node to a trusted node. A diagram of the trust relations in thefield of consumption activities is called a “trust network graph”. InFIG. 1, no trust value has yet been calculated: hence, FIG. 1 isreferred to as a “trust network graph without trust values”.

According to one aspect of the invention, “subjects” for which trustvalues are needed are first determined, and then “arrows” relating thesubjects with one another are determined. The apparatus of FIG. 7determines values associated with these “subjects”, “arrows” and weightstherefor are determined.

The following concepts are, for them, considered as the basis of thetrust relations existing in the targeted consumption activities.

(1) A customer desiring to purchase quality goods of crediblemanufacturers or shops.

(2) A customer consults experts or magazines which the customer trusts.

(3) A customer consults another customer who has actually used thegoods.

(4) A manufacturer or shop places an advertisement in a credibleadvertisement medium.

(5) A critic that rates quality goods, shops, and manufacturers.

Nodes having the terminal points of arrows extending from many nodes arein the nodes of the network diagram. Such nodes can be considered asnodes which have achieved much trust in the consumption activities.Nodes having terminal points of arrows extending from credible nodes arealso considered as nodes having high trust.

It is expected that a general customer takes an action on the faith ofcredible nodes as described above, for example, experts or advertisementmedia of magazines, etc. If the information about trust relations isused, the consumption activities of customers can be predicted on thebasis of the information or it is used as a hint for selling activities.

To detect a node having high trustworthiness, a trust value indicatingwhich node has a higher trustworthiness than the other nodes in thetrust network graph is calculated by the processor of FIG. 7. The trustvalue is calculated in accordance with the trust relation or the type ofthe relation, or so that nodes which are found to be credible in advanceare heavily weighted and nodes which are found not to be credible inadvance are lightly weighted. The trust value thus calculated isallocated to the node to construct a trust network graph with trustvalues.

The processor of FIG. 7 constructs a trust network graph on the basis ofthe above concepts in the following steps (FIG. 2):

10: select nodes/arrows;

20: create a trust network graph without trust values;

30: determine weights of nodes/arrows;

40: calculate the trust values of nodes; and

50: create a trust network graph with trust values.

The algorithm shown in FIG. 2 and performed by the processor if in FIG.7 is now described in detail.

Step 10: Select Nodes/Arrows

The “subjects” concerned with the trust network in the consumptionactivities are customers, experts (critics), advertisement media,magazines, web sites, goods, manufacturers, retailers, etc. These“subjects” are represented as nodes. The subjects (nodes) having anestablished trust relation are connected to one another with arrows. Anarrow extends from a trusting node to a trusted node. The trustrelations among the nodes are “evaluates a product”, “describes anarticle of a magazine”, “places an advertisement”, “buys a product”,“makes a product”, “introduces a product”, “hires an expert”, etc.

Step 20: Create Trust Network Graph Without Trust Values

In step 10, the processor of FIG. 7 creates a trust network graphcreated on the basis of the determined information (see FIG. 1).

Step 30: Determine Weights of Nodes/Arrows

Next, the processor of FIG. 7 collects relevant data are collected frominformation sources concerned with consumption activities to obtaintrust values for the nodes. For example, a page for a target product isaccessed from http://www.about.com to refer to a relevant web page.Introductions about the product or evaluation articles on magazines,advertisements, questionnaire results, etc. are available as data toobtain trust values. This information can be manually obtained, orautomatically or semi-automatically extracted by using an informationextracting machine to which natural language processing is applied orthe like.

When there is information concerning some trust relations among nodes,the processor of FIG. 7 weights the arrows on the basis of theinformation. Furthermore, when credible nodes are known in advance onthe basis of the information, higher weights are allocated to thosenodes.

Step 40: Calculate Trust Values of Nodes

The processor of FIG. 7 maps trust values of the respective, nodes onthe basis of the weights determined in step 30. The calculation methodis described later.

If the trust value of the node is high, the trust value node has higherreliability.

Step 50: Create Trust Network Graph With Trust Values

The processor of FIG. 7 maps values calculated in step 40 in the trustnetwork graph, whereby the information about high reliability subjects(nodes) can be grasped at a glace.

The trust value calculation method is described with reference to FIG. 3that includes the following steps:

110: select zero or more arrows among nodes;

120: determine initial weights of all arrows;

130: calculate the sum of the initial weights of all arrows directedfrom a node ui to a node uj;

140: calculate the sum of the initial weights of arrows starting fromnode ui;

150: calculate adjusted weight Pij=AW_(adj)(ui→uj);

160: define vector v;

170: calculate matrix E=e·v^(T), and calculate P′=cP+(1−c)E; and

180: calculate trust value TV(ui) of node ui.

The calculation of the trust values is described hereunder in detail.

Step 110: Select Zero or More Arrows Among Nodes

Arrows connecting nodes are now described. If no information onreliability exists between nodes, no arrow exists. Furthermore, thenumber of arrows between nodes is not limited to one. Consider theexample of a critic A writing an article about the function and price ofthe commercial article B that appears in magazine C at around the sametime an article concerning the performance of the commercial article Bby the critic A appears in a magazine C′. In such an example, two arrowsexist between the node representing the critic A and the noderepresenting the commercial article B. The arrows between the nodes aredetermined by the above information. In this example, an arrow extendsfrom the node ui to the node uj and is represented by A(ui→uj)k (k=1 tom).

Step 120: Determine Initial Weights for All Arrows

In accordance with the content of information existing between nodes,initial weights of arrows A(ui→uj)k (k=1 to m) are determined.

Step 130: Calculate the Sum of Initial Values of All Arrows ExtendingFrom Node ui to Node uj

When the sum of the initial values of the arrows from the node ui tonode uj is represented by AWacc(ui→uj), $\begin{matrix}\begin{matrix}\left\lbrack {{Equation}\quad 7} \right\rbrack \\{{{AWacc}\left( {{ui}->{uj}} \right)} = {\sum\limits_{k = 1}^{m}{{{AW}_{init}\left( {{ui}->{uj}} \right)}k}}}\end{matrix} & (1)\end{matrix}$

Step 140: Calculate the Sum of Initial Weights of Arrows Having Node uias a Starting Point

The sum of the initial values is represented by AWacc (ui).

Step 150: Calculate Adjusted Weight Pij as:(2) Pij=AWacc(ui→uj)/AWacc(ui)  [Equation 8]By this adjustment, the weight of the arrow between the respective nodestakes a value from 0 to 1, and is represented by Pij.

Step 160: Define Vector v

The vector v is a vector including one or more elements, each of whichrepresents a weight for a node and the sum of the respective elementsis 1. The processor pf of FIG. 7 adjusts the value of the vector v isconsidered as the degree of trust of the node, and is determined on thebasis of evaluations of articles in magazines, trust of individuals,ranking information of evaluating agencies, etc. to the node. The valueof the vector v is adjusted according to a desired object of a consumerto thereby obtain trust based on the object of the consumer.

Step 170: Calculate Matrix E=e·v^(T), and Calculate P′=cP+(1−c)E

The processor of FIG. 7 calculates matrix E=e·v^(T) by using a vector ein which all the elements are “1”, and P′=cP+(1−c)E is calculated. Here,c represents a constant having a value 0≦c≦1, the value of C c isdetermined experimentally.

Step 180: Calculate Trust Value TV(ui) of Node ui

The trust value TV(ui) of the node ui is defined as follows:$\begin{matrix}\begin{matrix}\left\lbrack {{Equation}\quad 9} \right\rbrack \\{{{TV}({ui})} = {\sum\limits_{{uj} \in {{BN}{({ui})}}}{{P^{\prime}\left( {{uj}->{ui}} \right)}{{TV}({uj})}}}}\end{matrix} & (3)\end{matrix}$where:(4) Uj εBN(Ui)  [Equation 10]represents a set of nodes that direct arrows to noce node ui. Theprocessor of FIG. 7 calculates an eigen vector (TV(ui), i=1 to n) ofExpression (3) (i.e., Equation 109) is calculated. By defining vectorX^(T)=(TV(u1), . . . , TV(un)) is in Expression (3) can be representedas follows:(5) {right arrow over (X)}=P′p^(T) {right arrow over (X)}^(T)  [Equation11]The processor of FIG. 7 calculates the vector {right arrow over (X)} iscalculated as an eigen vector to an eigen value “1” of the transposedmatrix of the matrix P′. Each value of the eigen vector {right arrowover (X)} corresponds to the trust value of each node. In these nodes, anode uj having a large value of TV (uj) is considered to be a node(subject) having high trust.

This embodiment is applied to the following assumed consumptionactivities by using the above calculation results.

(a) One manufacturer is more credible than other manufacturers andproducts made by the credible manufacturer are introduced orrecommended.

(b) Consumers can buy products made by the most credible manufactures atmost credible shops.

However, since the products do not necessarily serve as subjects, theabove example is rewritten so that there are indirect trust relationsthrough the products. For example, in case that an expert recommends aproduct, the expert is considered to trust the manufacturer who made theproduct.

Next, consider an example in which the above calculation result ismapped in the trust network graph. FIG. 4 is an example in which nodesand the trust values of the nodes are selected, and arrows are filledamong the nodes. In FIG. 4, a numbered box represents anode, and anincluded numeric value allocated to each node represents the calculatedtrust value of the node. The processor of FIG. 7 performs thecalculation under the condition that c=0.85 and the value of the vectorV is equal to “1/n” for all the nodes. The numeric values in parenthesesrepresent weights of arrows. In FIG. 4, “1.0” is used as the weight ofeach arrow.

In FIG. 5, “10.0” is used as the weights of arrows for the specificarrows (node 2→node 5, node 3→node 5, node 7→node 3, node 7→node 8). Theprocessor of FIG. 7 calculates the trust values of the respective nodeswhen the above value is used and the other conditions that are notchanged are shown. The processor changes the trust value of each node bythe weights of the arrows.

The processor changes the value of the node 1 in FIG. 5 from 0.366 inFIG. 4 to 0.3016, and the value of the node 4 in FIG. 5 from 0.378 inFIG. 4 to 0.287. In FIG. 5 the node 1 has a higher trust value than thenode 4 whereas in FIG. 4 the node 4 has a higher trust value than thenode 1. FIG. 6 shows the trust values of the respective nodes that theprocessor calculates on the condition that “3.0” is selected as thetrust value of a specific arrow (node 5→node 6) and the other conditionsare unchanged. The value of the node 2 in FIG. 6 is changed from 0.0545in FIG. 4 to 0.0665, and the value of the node 5 in FIG. 6 is changedfrom 0.055 in FIG. 4 to 0.0588. Although the node 5 has a higher trustvalue in FIG. 4, the trust value of the node 2 has a higher trust valuein FIG. 6.

By changing weights of nodes or arrows in consideration of taste profileinformation of individuals, trust values the processor calculates withconsideration of the taste profiles of the individuals are obtained. Forexample, there is described a case where the trend of the taste of someindividual is similar to the content of some fashion magazine A. Thevector v defined in step 160 of FIG. 3 is a vector in which each elementrepresents a weight for a node and the sum of all the elements is equalto 1. The value of the vector v changes in accordance with a desiredobject to obtain a trust value the processor calculates withconsideration of the desired object. By increasing the value of thevector v corresponding to the node corresponding to the fashion magazineA, the trust value of each node the processor calculates is obtained inconsideration of the trend of the fashion magazine A.

As another application, consider such individualization in which thedegree of the trust relations among subjects are known in advance or arerequired to be enhanced and is reflected in the trust value calculation.A case where the trust value of an arrow is changed is now described. Asdescribed above, there are one or more arrows between nodes having somerelation to each other. For example, the value associated with an arrowbetween a subject concerned with a specific magazine and another subjecthaving a trust relation with the subject can be increased. The processorcalculates trust value by considering the trust value of subscribers ofthe magazine concerned, by using the changed trust value associated withthe arrows. As a result, the products of the subject which areconsidered to be credible can be recommended or introduced, and betterproducts can be recommended or introduced to persons who subscribe tothe magazine. In this case, the degree of credibility is introduced bychanging the weights of the arrows among the nodes or changing theadjusted weight Pij. The calculation can be performed by using the trustrelations associated with individuals or groups to which the individualsbelong. Furthermore, trust values differ for different individual tasteprofiles such as age, sex, annual income, family structure, hobby,taste, etc. The processor can calculate trust values by changing thevector v shown in step 160 of FIG. 3 and the trust values of the arrows.

The invention is applicable to various fields: however, it isparticularly effective in the following fields.

(1) Promotion of Sales Advancing Activities

If it is known that a particular manufacturer is trusted more thanothers, the products made by the manufacturer areintroduced/recommended. Accordingly, sellers of the products of themanufacturers can effectively advance marketing and selling activities.

(2) Introduction/Recommendation of Products Matched With Requirements ofIndividuals (Personalization)

The products can be introduced/recommended on the basis of trustinformation of each subject based on general information and ondifferent taste information of each individual.

(3) Introduction/Recommendation of Products Matched With Requirements ofSpecific Group

For example, on the basis of information on subjects concerned with aspecific magazine and trust relations among the subjects, the processorcalculates trust values are calculated by considering the trustrelations inherent to subscribers of the magazine, and better productscan be recommended/introduced to persons subscribing to the magazine.

(4) Application to Information Filtering

The processor can perform a filtering function by selecting nodes havinghigher trust values on the basis of search results obtained by varioussearching systems. Furthermore, manufacturers and shops which have hightrust values can be preferentially introduced to customers who put ahigher premium on the trust of products and the trust of shops than theprices of the products.

While there have been described and illustrated specific embodiments ofthe invention, it will be clear that variations on the details of theembodiment specifically illustrated and described may be made withoutspecifically departing from the true spirit and scope of the inventionas defined in the appended claims.

1. A device for calculating trust values of n nodes (ui, i=1 to n): (a)means for relating nodes with one another with arrows when therespective nodes have corresponding relations with one another; (b)means for selecting Pij (i, j=1 to n) as a weight for an arrow directedfrom a node ui to a node uj on the basis of the corresponding relationbetween nodes; (c) means for calculating P′=cP+(1−c)E where c is aconstant, E is a predetermined matrix, P is a matrix in which an elementof row i and column j is represented by Pij; and (d) means forcalculating a trust value of each node (TV(ui), i=1 to n) on the basisof P′.
 2. The device according to claim 1, wherein the means forcalculating the trust value of each node (TV(ui), i=1 to n) is arrangedto calculate TV(ui) , for i=1 ton, as an eigen vector for an eigen value1 of p, T, where${{TV}({ui})} = {\sum\limits_{{uj} \in {{BN}{({ui})}}}{{P^{\prime}\left( {{uj}->{ui}} \right)}{{TV}({uj})}}}$where,Uj εBN(Ui)represents a set of nodes for emitting arrows that areincident on ui.
 3. The device according to claim 2, wherein the meansfor connecting the respective nodes with arrows includes: (a) means forconnecting a first node and a second node with zero or more arrows onthe basis of the corresponding relationship; (b) means for assigninginitial weight values to the arrows; (c) means for calculating the sum(first sum) of the initial values of all the arrows directed from thefirst node to the second node; (d) means for calculating the sum (secondsum) of the initial weight values of all the arrows having the firstnode as a starting point; and (e) means for dividing the first sum bythe second sum to calculate an adjusted weight, and achieving adjustedweights Pij for all the nodes in the same manner.
 4. The deviceaccording to claim 3, wherein the means for calculating P′ includes:means for (a) deriving a vector e in which all elements are equal to 1,(b) deriving a vector v in which the respective elements representweights for the nodes and the sum of the respective elements is equal to1, (c) calculating a matrix E=e·v^(T), and (d) calculating P′=cP+(1−c)E,where c is a constant satisfying 0≦c≦1.
 5. The device according to claim1, wherein the means for calculating P′ includes: means for (a) derivinga vector e in which all elements are equal to 1, (b) deriving a vector vin which the respective elements represent weights for the nodes and thesum of the respective elements is equal to 1, (c) calculating a matrixE=e·v^(T), and (d) calculating P′=cP+(1−c)E, where c is a constantsatisfying 0≦c≦1.
 6. The device according to claim 4, wherein the meansfor defining the vector v is arranged to define the vector v on thebasis of the degree of trust to the nodes.
 7. The device according toclaim 6, wherein the means for defining the vector v is arranged todefine the vector v on the basis of the degree of trust to the nodes. 8.A storage medium or storage device having a program for causing aprocessing arrangement to calculate trust values of n nodes (ui, i=1 ton) by causing the processing arrangement to execute a processcomprising: (a) relating nodes with one another with arrows when therespective nodes have corresponding relations with one another; (b)selecting Pij (i, j=1 to n) as a weight for an arrow directed from anode ui to a node uj on the basis of the corresponding relation betweenthe nodes; (c) calculating P′=cP+(1−c)E, where c is a constant, E is apredetermined matrix, and P is a matrix in which an element of row i andcolumn j is represented by Pij; and (d) calculating a trust value ofeach node (TV(ui), i=1 to n) on the basis of P′.
 9. The medium or deviceaccording to claim 8, wherein the trust value of each node (TV(ui), i=1to n) is caused to be calculated by calculating TV(ui), i=1 to n as aneigen vector for an eigen value 1 of P′^(T) where${{TV}({ui})} = {\sum\limits_{{uj} \in {{BN}{({ui})}}}{{P^{\prime}\left( {{uj}->{ui}} \right)}{{TV}({uj})}}}$where,Uj εBN(Ui)represents a set of nodes for emitting arrows that areincident on ui.
 10. The medium or device according to claim 9, whereinthe respective nodes are caused to be connected with arrows by causingthe processor arrangement to: (a) connect a first node and a second nodewith zero or more arrows on the basis of the corresponding relationship;(b) assign initial weight values to the arrows; (c) calculate the sum(first sum) of the initial values of all the arrows directed from thefirst node to the second node; (d) calculate the sum (second sum) of theinitial weight values of all the arrows having the first node as astarting point; (e) divide the first sum by the second sum to calculatean adjusted weight; and (f) achieve adjusted weights Pij for all thenodes in the same manner.
 11. The medium or device according to claim10, wherein the program causes the processor arrangement to calculate P′by: deriving a vector e in which all the elements are equal to 1,deriving a vector v in which the respective elements represent weightsfor nodes and the sum of the respective elements is equal to 1,calculating a matrix E=e·v^(T), and calculating P′=cP+(1−c)E, where c isa constant satisfying 0≦c≦1.
 12. The medium or device according to claim11, wherein the program causes the processor to derive the vector v onthe basis of the degree of trust to the nodes.
 13. The medium or deviceaccording to claim 8, wherein the respective nodes are caused to beconnected with arrows by causing the processor arrangement to: (a)connect a first node and a second node with zero or more arrows on thebasis of the corresponding relationship; (b) assign initial weightvalues to the arrows; (c) calculate the sum (first sum) of the initialvalues of all the arrows directed from the first node to the secondnode; (d) calculate the sum (second sum) of the initial weight values ofall the arrows having the first node as a starting point; (e) divide thefirst sum by the second sum to calculate an adjusted weight; and (f)achieve adjusted weights Pij for all the nodes in the same manner. 14.The medium or device according to claim 8, wherein the program causesthe processor arrangement to calculate P′ by: deriving a vector e inwhich all the elements are equal to 1, deriving a vector v in which therespective elements represent weights for nodes and the sum of therespective elements is equal to 1, calculating a matrix E=e·v^(T), andcalculating P′=cP+(1−c)E, where c is a constant satisfying 0≦c≦1. 15.The medium or device according to claim 8, wherein the program causesthe processor to derive the vector v on the basis of the degree of trustto the nodes.
 16. A method of calculating trust values of n nodes (ui,i=1 to n), the nodes having corresponding relations with one anotherbeing related with one another with arrows; the method comprising: (a)assigning Pij(i, j=1 to n) as a weight for an arrow directed from nodeui to node uj on the basis of the corresponding relation between thenodes; (b) calculating P′=cP+(1−c)E, where c is a constant, E is apredetermined matrix, P is a matrix in which an element of row i andcolumn j is represented by Pij; and (c) calculating the trust value ofeach node (TV(ui), i=1 to n) on the basis of P′.
 17. The methodaccording to claim 16, wherein the step of calculating the trust valueof each node (TV(ui), i=1 to n) includes calculating TV(ui), i=1 to n asan eigen vector for an eigen value 1 of P′^(T) where${{TV}({ui})} = {\sum\limits_{{uj} \in {{BN}{({ui})}}}{{P^{\prime}\left( {{uj}->{ui}} \right)}{{TV}({uj})}}}$where,Uj εBN(Ui),represents a set of the nodes that emit arrows that areincident on node ui.
 18. The method according to claims 17, wherein therespective nodes are connected with arrows by a method including: (a)connecting a first node and a second node with zero or more arrows onthe basis of the corresponding relationship; (b) assigning initialweight values to the arrows; (c) calculating the sum (first sum) of theinitial values of all the arrows directed from the first node to thesecond node; (d) calculating the sum (second sum) of the initial weightvalues of all the arrows having the first node as a starting point; and(e) calculating an adjusted weight by dividing the first sum by thesecond sum; and (f) achieving adjusted weights Pij for all the nodes inthe same manner.
 19. The method according to claim 18, wherein the stepof calculating P′ includes: deriving a vector e in which all theelements thereof are equal to 1, deriving a vector v in which therespective elements represent weights for nodes and the sum of therespective elements is equal to 1, calculating a matrix E=e·v^(T), andcalculating P′=cP+(1−c)E, where c is a constant satisfying 0≦c≦1. 20.The method according to claim 19, wherein the vector v is derived on thebasis of the degree of trust to the nodes.
 21. The method according toclaim 16, wherein the respective nodes are connected with arrows by amethod including: (a) connecting a first node and a second node withzero or more arrows on the basis of the corresponding relationship; (b)assigning initial weight values to the arrows; (c) calculating the sum(first sum) of the initial values of all the arrows directed from thefirst node to the second node; (d) calculating the sum (second sum) ofthe initial weight values of all the arrows having the first node as astarting point; and (e) calculating an adjusted weight by dividing thefirst sum by the second sum; and (f) achieving adjusted weights Pij forall the nodes in the same manner.
 22. The method according to claim 16,wherein the step of calculating P′ includes: deriving a vector e inwhich all the elements thereof are equal to 1, deriving a vector v inwhich the respective elements represent weights for nodes and the sum ofthe respective elements is equal to 1, calculating a matrix E=e·v^(T),and calculating P′=cP+(1−c)E, where c is a constant satisfying 0≦c≦1.23. The method according to claim 16, wherein the vector v is derived onthe basis of the degree of trust to the nodes.
 24. A processorarrangement for performing the method of claim
 16. 25. A processorarrangement for performing the method of claim
 21. 26. A processorarrangement for performing the method of claim
 22. 27. A processorarrangement for performing the method of claim
 24. 28. The device ofclaim 1 wherein the nodes are selected from the group includingcustomers, critics, advertisement media, magazines, web sites, goods,manufacturers, and retailers.
 29. The medium or storage device of claim8 wherein the nodes are selected from the group including customers,critics, advertisement media, magazines, web sites, goods,manufacturers, and retailers.
 30. The method of claim 16 wherein thenodes are selected from the group including customers, critics,advertisement media, magazines, web sites, goods; manufacturers, andretailers.
 31. The processor arrangement of claim 24 wherein the nodesare selected from the group including customers, critics, advertisementmedia, magazines, web sites, goods, manufacturers, and retailers.